Abstract [en]

Biologic therapies have revolutionized the treatment of rheumatoid arthritis, a common chronic inflammatory disease, mainly characterized by the chronic inflammation of the joints. The activity and progression of the disease are highly variable, both between subjects and between the successive assessments for the same subject. Standardized assessments of clinical variables have been developed to reflect the disease activity and evaluate new therapies. Pharmacokinetics-pharmacodynamic (PKPD) models and methods for analyzing the generated time-course data are needed to improve the interpretation of the clinical trials’ outcomes, and to describe the variability between subjects, including patients characteristics, disease factors and the use of concomitant treatments that may affect the response to treatment. In addition, good simulation properties are also desirable for predicting clinical responses for various populations or for different dosing schedules. The aim of this thesis was to develop methods and models for analyzing pharmacokinetic and pharmacokinetic-pharmacodynamic (PKPD) data from rheumatoid arthritis patients, illustrated by treatment with a new anti-TNFα biologic drug under clinical development, certolizumab pegol.

Two models were developed that characterized the relationship between the exposure to the drug and the efficacy ACR variables that represent improvement of the disease; a logistic-type Markov model for 20% improvement (ACR20) and a continuous-type Markov model for simultaneous analysis of 20% (ACR20), 50% (ACR50) and 70% (ACR70) improvement. Both models accounted for the within-subjects correlation in the successive clinical assessments and were able to capture the observed ACR responses over time. Simulations from these models of the ACR20 response rate supported dosing regimens of 400 mg at weeks 0, 2 and 4 to achieve a rapid onset of response to the treatment, followed by 200 mg every 2 weeks, or alternative maintenance regimen of 400 mg every 4 weeks.

The immunogenicity induced by the biologic drug was characterized by a time to event model describing the time to appearance of antibodies directed against the drug. The immunogenicity was predicted to appear mainly during the first 3 months following the start of the treatment and to be reduced at higher trough concentrations of CZP, as well as with concomitant administration of MTX.

The full time-course of sequential events, such as dose-exposure-efficacy relations, is most accurately described by a simultaneous analysis of all data. However, due to the complexity and runtime limitations of such an analysis, alternatives are often used. In this thesis, a method, IPPSE, was developed and compared to the reference simultaneous method and to existing alternative methods. The IPPSE method was shown to provide accuracy and precision of estimates similar to the simultaneous method, but with easier implementation and shorter run times.

In conclusion, two PKPD models and one immunogenicity model were developed for evaluation of the response of a biologic drug against rheumatoid arthritis that allowed accurate analysis and simulation of clinical trial data, as well as serving as examples for how a model-informed basis for decisions about biological drugs can be created.

Abstract [en]

The American College of Rheumatology (ACR) 20% preliminary definition of improvement in rheumatoid arthritis (RA) (ACR20) is widely used in clinical trials to assess response to treatment. The objectives of this analysis were to develop an exposure-response model of ACR20 in subjects receiving treatment with certolizumab pegol and to predict clinical outcomes following various treatment schedules. At each visit, subjects were classified as being ACR20 responders or ACR20 nonresponders or as having dropped out. A Markov mixed-effects model was developed to investigate the effects of the drug on the transitions between the three defined states. Increasing certolizumab pegol exposure predicted an increasing probability of becoming a responder and remaining a responder, as well as a reduced probability of dropping out of treatment. Data from simulations of the ACR20 response rate support the use of dosing regimens of 400 mg at weeks 0, 2, and 4 followed by 200 mg every 2 weeks, or an alternative maintenance regimen of 400 mg every 4 weeks.

Abstract [en]

The Markovian approach has been proposed to model ACR response (ACR20, ACR50 or ACR70) reported in rheumatoid arthritis clinical trials to account for the dependency of the scores over time. However, dichotomizing the composite ACR assessment discards much information. Here we propose a new approach for modeling together the 3 thresholds: a continuous-time Markov exposure-response model was developed, based on data from 5 placebo-controlled certolizumab pegol clinical trials. This approach allowsadequate prediction of individual ACR20/50/70 time-response, even for non-periodic observations. An exposure-response was established over a large range of licensed and unlicensed doses including phase II dose-ranging data. Simulations from the model (50 to 400 mg every other week) illustrated the range and sustainability of response (ACR20: 56 to 68%, ACR50: 27 to 42%, ACR70: 11 to 22% at week 24) with maximum clinical effect achieved at the recommended maintenance dose of 200 mg every other week.

Karlsson, Mats

Abstract [en]

The advent of biologic therapies improved greatly the treatment of chronic inflammatory diseases. However, these drugs may induce an unwanted specific immune response directed against them. The immunogenicity of biologic drugs is of concern for all healthcare stakeholders and methods for better characterization are needed, as well as means to minimize the occurrence of anti-drug antibodies (ADA). The objective of this analysis was to develop a time-to-event model for characterizing the immunogenicity of certolizumab pegol in subjects suffering from rheumatoid arthritis included in phase II and phase III clinical trials. The time to formation of ADA was analyzed based on previously estimated individual certolizumab pegol concentration-time profiles. In the final model, the hazard of developing ADAs was predicted to depend on time since start of treatment, trough drug concentration in the preceding dosing interval and the concomitant use of methotrexate. Specific study effects were added into the model in order to capture the trends in two studies that remained non-explained after the covariates had been tested. The model may be used to test strategies for minimizing the immunogenicity, such as the use of a loading dose or the reduction of the intervals between dosing.

Abstract [en]

The aim of this study is to present and evaluate an alternative sequential method to perform population pharmacokinetic-pharmacodynamic (PKPD) analysis. Simultaneous PKPD analysis (SIM) is generally considered the reference method but may be computationally burdensome and time consuming. Evaluation of alternative approaches aims at speeding up the computation time and stabilizing the estimation of the models, while estimating the model parameters with good enough precision. The IPPSE method presented here uses the individual PK parameter estimates and their uncertainty (SE) to propagate the PK information to the PD estimation step, while the IPP method uses the individual PK parameters only and the PPP&D method utilizes the PK data. Data sets (n = 200) with various study designs were simulated according to a one-compartment PK model and a direct Emax PD model. The study design of each dataset was randomly selected. The same PK and PD models were fitted to the simulated observations using the SIM, IPP, PPP&D and IPPSE methods. The performances of the methods were compared with respect to estimation precision and bias, and computation time. Estimated precision and bias for the IPPSE method were similar to that of SIM and PPP&D, while IPP had higher bias and imprecision. Compared with the SIM method, IPPSE saved more computation time (61%) than PPP&D (39%), while IPP remained the fastest method (86% run time saved). The IPPSE method is a promising alternative for PKPD analysis, combining the advantages of the SIM (higher precision and lower bias of parameter estimates) and the IPP (shorter run time) methods.